I. Introduction to Steady State Visual Evoked Potentials (SSVEP) based Brain-Computer Interfaces (BCI)

II. Classification using:

1. Canonical Correlation Analysis (CCA)
2. Magnitude Spectrum Features and Convolutional Neural Networks (M-CNN)
3. Complex Spectrum Features and Convolutional Neural Networks (C-CNN)

Additional Information and similar other studies can be found in my Masters Thesis: https://uwspace.uwaterloo.ca/handle/10012/14881

Dataset Reference: 12-Class publicly available SSVEP EEG Dataset Dataset URL: https://github.com/mnakanishi/12JFPM_SSVEP/tree/master/data

Please cite the following article: https://iopscience.iop.org/article/10.1088/1741-2552/ab6a67/pdf

@article{10.1088/1741-2552/ab6a67, author={Aravind Ravi and Nargess Heydari Beni and Jacob Manuel and Ning Jiang}, title={Comparing user-dependent and user-independent training of CNN for SSVEP BCI}, journal={Journal of Neural Engineering}, url={https://iopscience.iop.org/article/10.1088/1741-2552/ab6a67}, year={2020}, }